Summary of Make-an-agent: a Generalizable Policy Network Generator with Behavior-prompted Diffusion, by Yongyuan Liang et al.
Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion
by Yongyuan Liang, Tingqiang Xu, Kaizhe Hu, Guangqi Jiang, Furong Huang, Huazhe Xu
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Make-An-Agent, a novel policy parameter generator that leverages conditional diffusion models to generate control policies from a single demonstration of desired behaviors. The policy generator uses behavior embeddings that encode trajectory information to synthesize latent parameter representations, which can be decoded into policy networks. Trained on policy network checkpoints and their corresponding trajectories, the generation model demonstrates remarkable versatility and scalability on multiple tasks, with strong generalization ability on unseen tasks. The paper showcases its efficacy and efficiency on various domains and tasks, including locomotion tasks on real-world robots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Make-An-Agent is a new way to create control policies for machines using just one example of how the machine should behave. It works by looking at what makes different behaviors similar or different, and then uses that information to generate new policies. The paper shows that this method can be very effective on different types of tasks and with different machines. This could have big implications for how we teach robots and other machines to do new things. |
Keywords
» Artificial intelligence » Diffusion » Generalization
